In this paper is being described a possible use of
virtualization technology in teaching computer networks. The
virtualization can be used as a suitable tool for creating virtual
network laboratories, supplementing the real laboratories and
network simulation software in teaching networking concepts. It will
be given a short description of characteristic projects in the area of
virtualization technology usage in network simulation, network
experiments and engineering education. A method for implementing
laboratory has also been explained, together with possible laboratory
usage and design of laboratory exercises. At the end, the laboratory
test results of virtual laboratory are presented as well.

This work presents a matched field processing (MFP)
algorithm based on Dopplerlet transform for estimating the motion
parameters of a sound source moving along a straight line and with a
constant speed by using a piecewise strategy, which can significantly
reduce the computational burden. Monte Carlo simulation results and
an experimental result are presented to verify the effectiveness of the
algorithm advocated.

Monitoring lightning electromagnetic pulses (sferics)
and other terrestrial as well as extraterrestrial transient radiation signals
is of considerable interest for practical and theoretical purposes
in astro- and geophysics as well as meteorology. Managing a continuous
flow of data, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.

There is increasing evidence that earthquakes produce electromagnetic signals observable at the surface in the extremely low to very low freqency (ELF - VLF) range often in advance to the main event. These precursors are candidates for prediction purposes. Laboratory experiments con´¼ürm that material under load emits an electromagnetic signature, the detailed generation mechanisms how- ever are not well understood yet.

This paper proposes a method for speckle reduction in
medical ultrasound imaging while preserving the edges with the
added advantages of adaptive noise filtering and speed. A nonlinear
image diffusion method that incorporates local image parameter,
namely, scatterer density in addition to gradient, to weight the
nonlinear diffusion process, is proposed. The method was tested for
the isotropic case with a contrast detail phantom and varieties of
clinical ultrasound images, and then compared to linear and some
other diffusion enhancement methods. Different diffusion parameters
were tested and tuned to best reduce speckle noise and preserve
edges. The method showed superior performance measured both
quantitatively and qualitatively when incorporating scatterer density
into the diffusivity function. The proposed filter can be used as a
preprocessing step for ultrasound image enhancement before
applying automatic segmentation, automatic volumetric calculations,
or 3D ultrasound volume rendering.

In contrast to existing methods which do not take into
account multiconnectivity in a broad sense of this term, we develop
mathematical models and highly effective combination (BIEM and
FDM) numerical methods of calculation of stationary and quasistationary
temperature field of a profile part of a blade with
convective cooling (from the point of view of realization on PC). The
theoretical substantiation of these methods is proved by appropriate
theorems. For it, converging quadrature processes have been
developed and the estimations of errors in the terms of A.Ziqmound
continuity modules have been received.
For visualization of profiles are used: the method of the least
squares with automatic conjecture, device spline, smooth
replenishment and neural nets. Boundary conditions of heat exchange
are determined from the solution of the corresponding integral
equations and empirical relationships. The reliability of designed
methods is proved by calculation and experimental investigations
heat and hydraulic characteristics of the gas turbine first stage nozzle
blade.

Oxygen transfer, the process by which oxygen is
transferred from the gaseous to liquid phase, is a vital part of the
waste water treatment process. Because of low solubility of
oxygen and consequent low rate of oxygen transfer, sufficient
oxygen to meet the requirement of aerobic waste does not enter
through normal surface air water interface. Many theories have
come up in explaining the mechanism of gas transfer and
absorption of non-reacting gases in a liquid, of out of which, Two
film theory is important. An exiting mathematical model
determines approximate value of Overall Gas Transfer coefficient.
The Overall Gas Transfer coefficient, in case of Penetration theory,
is 1.13 time more than that obtained in case of Two film theory.
The difference is due to the difference in assumptions in the two
theories.
The paper aims at development of mathematical model which
determines the value of Overall Gas Transfer coefficient with
greater accuracy than the existing model.

An attractor neural network on the small-world topology
is studied. A learning pattern is presented to the network, then
a stimulus carrying local information is applied to the neurons and
the retrieval of block-like structure is investigated. A synaptic noise
decreases the memory capability. The change of stability from local
to global attractors is shown to depend on the long-range character
of the network connectivity.

The model of neural networks on the small-world
topology, with metric (local and random connectivity) is investigated.
The synaptic weights are random, driving the network towards a
chaotic state for the neural activity. An ordered macroscopic neuron
state is induced by a bias in the network connections. When the
connections are mainly local, the network emulates a block-like
structure. It is found that the topology and the bias compete to
influence the network to evolve into a global or a block activity
ordering, according to the initial conditions.

This paper presents the performance analysis of
space-time trellis codes in orthogonal frequency division multiplexing
systems (STTC-OFDMs) over quasi-static frequency
selective fading channels. In particular, the effect of channel delay
distributions on the code performance is discussed. For a STTCOFDM
over multiple-tap channels, two extreme conditions that
produce the largest minimum determinant are highlighted. The
analysis also proves that the corresponding coding gain increases
with the maximum tap delay. The performance of STTC-OFDM,
under various channel conditions, is evaluated by simulation. It
is shown that the simulation results agree with the performance
analysis.

The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.

In this paper, we propose an approach of unsupervised
segmentation with fuzzy connectedness. Valid seeds are first specified
by an unsupervised method based on scale space theory. A region is
then extracted for each seed with a relative object extraction method of
fuzzy connectedness. Afterwards, regions are merged according to the
values between them of an introduced measure. Some theorems and
propositions are also provided to show the reasonableness of the
measure for doing mergence. Experiment results on a synthetic image,
a color image and a large amount of MR images of our method are
reported.

Ringing effect is one of the most annoying visual
artifacts in digital video. It is a significant factor of subjective quality
deterioration. However, there is a widely-accepted misunderstanding
of its cause. In this paper, we propose a reasonable interpretation of the
cause of ringing effect. Based on the interpretation, we suggest further
two methods to reduce ringing effect in DCT-based video coding. The
methods adaptively adjust quantizers according to video features. Our
experiments proved that the methods could efficiently improve
subjective quality with acceptable additional computing costs.

Color image segmentation can be considered as a
cluster procedure in feature space. k-means and its adaptive
version, i.e. competitive learning approach are powerful tools
for data clustering. But k-means and competitive learning suffer
from several drawbacks such as dead-unit problem and need to
pre-specify number of cluster. In this paper, we will explore to
use competitive and cooperative learning approach to perform
color image segmentation. In competitive and cooperative
learning approach, seed points not only compete each other, but
also the winner will dynamically select several nearest
competitors to form a cooperative team to adapt to the input
together, finally it can automatically select the correct number
of cluster and avoid the dead-units problem. Experimental
results show that CCL can obtain better segmentation result.

The myoelectric signal (MES) is one of the Biosignals
utilized in helping humans to control equipments. Recent approaches
in MES classification to control prosthetic devices employing pattern
recognition techniques revealed two problems, first, the classification
performance of the system starts degrading when the number of
motion classes to be classified increases, second, in order to solve the
first problem, additional complicated methods were utilized which
increase the computational cost of a multifunction myoelectric
control system. In an effort to solve these problems and to achieve a
feasible design for real time implementation with high overall
accuracy, this paper presents a new method for feature extraction in
MES recognition systems. The method works by extracting features
using Wavelet Packet Transform (WPT) applied on the MES from
multiple channels, and then employs Fuzzy c-means (FCM)
algorithm to generate a measure that judges on features suitability for
classification. Finally, Principle Component Analysis (PCA) is
utilized to reduce the size of the data before computing the
classification accuracy with a multilayer perceptron neural network.
The proposed system produces powerful classification results (99%
accuracy) by using only a small portion of the original feature set.

Biological reactions of individuals of a testing animal
to toxic substance are unique and can be used as an indication of the
existing of toxic substance. However, to distinguish such phenomenon
need a very complicate system and even more complicate to analyze
data in 3 dimensional. In this paper, a system to evaluate in vitro
biological activities to acute toxicity of stochastic self-affine
non-stationary signal of 3D goldfish swimming by using fractal
analysis is introduced. Regular digital camcorders are utilized by
proposed algorithm 3DCCPC to effectively capture and construct 3D
movements of the fish. A Critical Exponent Method (CEM) has been
adopted as a fractal estimator. The hypothesis was that the swimming
of goldfish to acute toxic would show the fractal property which
related to the toxic concentration. The experimental results supported
the hypothesis by showing that the swimming of goldfish under the
different toxic concentration has fractal properties. It also shows that
the fractal dimension of the swimming related to the pH value of FD Ôëê
0.26pH + 0.05. With the proposed system, the fish is allowed to swim
freely in all direction to react to the toxic. In addition, the trajectories
are precisely evaluated by fractal analysis with critical exponent
method and hence the results exhibit with much higher degree of
confidence.

Clusters of microcalcifications in mammograms are an
important sign of breast cancer. This paper presents a complete
Computer Aided Detection (CAD) scheme for automatic detection of
clustered microcalcifications in digital mammograms. The proposed
system, MammoScan μCaD, consists of three main steps. Firstly
all potential microcalcifications are detected using a a method for
feature extraction, VarMet, and adaptive thresholding. This will also
give a number of false detections. The goal of the second step,
Classifier level 1, is to remove everything but microcalcifications.
The last step, Classifier level 2, uses learned dictionaries and sparse
representations as a texture classification technique to distinguish
single, benign microcalcifications from clustered microcalcifications,
in addition to remove some remaining false detections. The system
is trained and tested on true digital data from Stavanger University
Hospital, and the results are evaluated by radiologists. The overall
results are promising, with a sensitivity > 90 % and a low false
detection rate (approx 1 unwanted pr. image, or 0.3 false pr. image).

This paper presents a hand vein authentication system
using fast spatial correlation of hand vein patterns. In order to
evaluate the system performance, a prototype was designed and a
dataset of 50 persons of different ages above 16 and of different
gender, each has 10 images per person was acquired at different
intervals, 5 images for left hand and 5 images for right hand. In
verification testing analysis, we used 3 images to represent the
templates and 2 images for testing. Each of the 2 images is matched
with the existing 3 templates. FAR of 0.02% and FRR of 3.00 %
were reported at threshold 80. The system efficiency at this threshold
was found to be 99.95%. The system can operate at a 97% genuine
acceptance rate and 99.98 % genuine reject rate, at corresponding
threshold of 80. The EER was reported as 0.25 % at threshold 77. We
verified that no similarity exists between right and left hand vein
patterns for the same person over the acquired dataset sample.
Finally, this distinct 100 hand vein patterns dataset sample can be
accessed by researchers and students upon request for testing other
methods of hand veins matching.

A higher order spline interpolated contour obtained
with up-sampling of homogenously distributed coordinates for
segmentation of kidney region in different classes of ultrasound
kidney images has been developed and presented in this paper. The
performance of the proposed method is measured and compared with
modified snake model contour, Markov random field contour and
expert outlined contour. The validation of the method is made in
correspondence with expert outlined contour using maximum coordinate
distance, Hausdorff distance and mean radial distance
metrics. The results obtained reveal that proposed scheme provides
optimum contour that agrees well with expert outlined contour.
Moreover this technique helps to preserve the pixels-of-interest
which in specific defines the functional characteristic of kidney. This
explores various possibilities in implementing computer-aided
diagnosis system exclusively for US kidney images.

Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.

As the use of registration packages spreads, the number of the aligned image pairs in image databases (either by manual or automatic methods) increases dramatically. These image pairs can serve as a set of training data. Correspondingly, the images that are to be registered serve as testing data. In this paper, a novel medical image registration method is proposed which is based on the a priori knowledge of the expected joint intensity distribution estimated from pre-aligned training images. The goal of the registration is to find the optimal transformation such that the distance between the observed joint intensity distribution obtained from the testing image pair and the expected joint intensity distribution obtained from the corresponding training image pair is minimized. The distance is measured using the divergence measure based on Tsallis entropy. Experimental results show that, compared with the widely-used Shannon mutual information as well as Tsallis mutual information, the proposed method is computationally more efficient without sacrificing registration accuracy.

Semiconductor nanomaterials like TiO2 nanoparticles
(TiO2-NPs) approximately less than 100 nm in diameter have become
a new generation of advanced materials due to their novel and
interesting optical, dielectric, and photo-catalytic properties. With the
increasing use of NPs in commerce, to date few studies have
investigated the toxicological and environmental effects of NPs.
Motivated by the importance of TiO2-NPs that may contribute to the
cancer research field especially from the treatment prospective
together with the fractal analysis technique, we have investigated the
effect of TiO2-NPs on colony morphology in the dark condition
using fractal dimension as a key morphological characterization
parameter. The aim of this work is mainly to investigate the cytotoxic
effects of TiO2-NPs in the dark on the growth of human cervical
carcinoma (HeLa) cell colonies from morphological aspect. The in
vitro studies were carried out together with the image processing
technique and fractal analysis. It was found that, these colonies were
abnormal in shape and size. Moreover, the size of the control
colonies appeared to be larger than those of the treated group. The
mean Df +/- SEM of the colonies in untreated cultures was
1.085±0.019, N= 25, while that of the cultures treated with TiO2-NPs
was 1.287±0.045. It was found that the circularity of the control
group (0.401±0.071) is higher than that of the treated group
(0.103±0.042). The same tendency was found in the diameter
parameters which are 1161.30±219.56 μm and 852.28±206.50 μm
for the control and treated group respectively. Possible explanation of
the results was discussed, though more works need to be done in
terms of the for mechanism aspects. Finally, our results indicate that
fractal dimension can serve as a useful feature, by itself or in
conjunction with other shape features, in the classification of cancer
colonies.

The amount of the information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. The predominance of biological information such as protein sequence similarity in the biological information sea is key information for detecting protein evolutionary relationship. Protein sequence similarity typically implies homology, which in turn may imply structural and functional similarities. In this work, we propose, a learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein sequence into fixed-dimensional representative feature vectors. Each feature vector records the sensitivity of a protein sequence to a set of amino acids substrings generated from the protein sequences of interest. These features are then used in conjunction with support vector machines for the detection of the protein remote homology. The proposed method is tested and evaluated on two different benchmark protein datasets and it-s able to deliver improvements over most of the existing homology detection methods.

This paper presents a simple and sensitive kinetic
spectrophotometric method for the determination of ramipril in
commercial dosage forms. The method is based on the reaction of the
drug with 1-chloro-2,4-dinitrobenzene (CDNB) in dimethylsulfoxide
(DMSO) at 100 ± 1ºC. The reaction is followed
spectrophotometrically by measuring the rate of change of the
absorbance at 420 nm. Fixed-time (ΔA) and equilibrium methods are
adopted for constructing the calibration curves. Both the calibration
curves were found to be linear over the concentration ranges 20 - 220
μg/ml. The regression analysis of calibration data yielded the linear
equations: Δ A = 6.30 × 10-4 + 1.54 × 10-3 C and A = 3.62 × 10-4 +
6.35 × 10-3 C for fixed time (Δ A) and equilibrium methods,
respectively. The limits of detection (LOD) for fixed time and
equilibrium methods are 1.47 and 1.05 μg/ml, respectively. The
method has been successfully applied to the determination of ramipril
in commercial dosage forms. Statistical comparison of the results
shows that there is no significant difference between the proposed
methods and Abdellatef-s spectrophotometric method.

In this paper we have proposed a methodology to
develop an amperometric biosensor for the analysis of glucose
concentration using a simple microcontroller based data acquisition
system. The work involves the development of Detachable
Membrane Unit (enzyme based biomembrane) with immobilized
glucose oxidase on the membrane and interfacing the same to the
signal conditioning system. The current generated by the biosensor
for different glucose concentrations was signal conditioned, then
acquired and computed by a simple AT89C51-microcontroller. The
optimum operating parameters for the better performance were found
and reported. The detailed performance evaluation of the biosensor
has been carried out. The proposed microcontroller based biosensor
system has the sensitivity of 0.04V/g/dl, with a resolution of
50mg/dl. It has exhibited very good inter day stability observed up to
30 days. Comparing to the reference method such as HPLC, the
accuracy of the proposed biosensor system is well within ± 1.5%.
The system can be used for real time analysis of glucose
concentration in the field such as, food and fermentation and clinical
(In-Vitro) applications.

Analysis of heart rate variability (HRV) has become a
popular non-invasive tool for assessing the activities of autonomic
nervous system. Most of the methods were hired from techniques
used for time series analysis. Currently used methods are time
domain, frequency domain, geometrical and fractal methods. A new
technique, which searches for pattern repeatability in a time series, is
proposed for quantifying heart rate (HR) time series. These set of
indices, which are termed as pattern repeatability measure and
pattern repeatability ratio are able to distinguish HR data clearly
from noise and electroencephalogram (EEG). The results of analysis
using these measures give an insight into the fundamental difference
between the composition of HR time series with respect to EEG and
noise.

Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.

The aim of this paper is to present a new method
which can be used for progressive transmission of electrocardiogram
(ECG). The idea consists in transforming any ECG signal to an
image, containing one beat in each row. In the first step, the beats are
synchronized in order to reduce the high frequencies due to inter-beat
transitions. The obtained image is then transformed using a discrete
version of Radon Transform (DRT). Hence, transmitting the ECG,
leads to transmit the most significant energy of the transformed
image in Radon domain. For decoding purpose, the receptor needs to
use the inverse Radon Transform as well as the two synchronization
frames.
The presented protocol can be adapted for lossy to lossless
compression systems. In lossy mode we show that the compression
ratio can be multiplied by an average factor of 2 for an acceptable
quality of reconstructed signal. These results have been obtained on
real signals from MIT database.

In this work, we consider a deterministic model for
the transmission of leptospirosis which is currently spreading in the
Thai population. The SIR model which incorporates the features of
this disease is applied to the epidemiological data in Thailand. It is
seen that the numerical solutions of the SIR equations are in good
agreement with real empirical data. Further improvements are
discussed.

As a popular rank-reduced vector space approach,
Latent Semantic Indexing (LSI) has been used in information
retrieval and other applications. In this paper, an LSI-based content
vector model for text classification is presented, which constructs
multiple augmented category LSI spaces and classifies text by their
content. The model integrates the class discriminative information
from the training data and is equipped with several pertinent feature
selection and text classification algorithms. The proposed classifier
has been applied to email classification and its experiments on a
benchmark spam testing corpus (PU1) have shown that the approach
represents a competitive alternative to other email classifiers based
on the well-known SVM and naïve Bayes algorithms.

The need for Information Security in organizations, regardless of their type and size, is being addressed by emerging standards and recommended best practices. The various standards and practices which evolved in recent years and are still being developed and constantly revised, address the issue of Information Security from different angles. This paper attempts to provide an overview of Information Security Standards and Practices by briefly discussing some of the most popular ones. Through a comparative study of their similarities and differences, some insight can be obtained on how their combination may lead to an increased level of Information Security.

Today, Genetic Algorithm has been used to solve
wide range of optimization problems. Some researches conduct on
applying Genetic Algorithm to text classification, summarization
and information retrieval system in text mining process. This
researches show a better performance due to the nature of Genetic
Algorithm. In this paper a new algorithm for using Genetic
Algorithm in concept weighting and topic identification, based on
concept standard deviation will be explored.

This paper provides a new approach to solve the motion planning problems of flying robots in uncertain 3D dynamic environments. The robots controlled by this method can adaptively choose the fast way to avoid collision without information about the shapes and trajectories of obstacles. Based on sphere coordinates the new method accomplishes collision avoidance of flying robots without any other auxiliary positioning systems. The Self-protection System gives robots self-protection abilities to work in uncertain 3D dynamic environments. Simulations illustrate the validity of the proposed method.

This paper describes a complex energy signal model
that is isomorphic with digital human fingerprint images. By using
signal models, the problem of fingerprint matching is transformed
into the signal processing problem of finding a correlation between
two complex signals that differ by phase-rotation and time-scaling. A
technique for minutiae matching that is independent of image
translation, rotation and linear-scaling, and is resistant to missing
minutiae is proposed. The method was tested using random data
points. The results show that for matching prints the scaling and
rotation angles are closely estimated and a stronger match will have a
higher correlation.

A new approach for timestamp ordering problem in
serializable schedules is presented. Since the number of users using
databases is increasing rapidly, the accuracy and needing high
throughput are main topics in database area. Strict 2PL does not
allow all possible serializable schedules and so does not result high
throughput. The main advantages of the approach are the ability to
enforce the execution of transaction to be recoverable and the high
achievable performance of concurrent execution in central databases.
Comparing to Strict 2PL, the general structure of the algorithm is
simple, free deadlock, and allows executing all possible serializable
schedules which results high throughput. Various examples which
include different orders of database operations are discussed.

The paper presents an on-line recognition machine
(RM) for continuous/isolated, dynamic and static gestures that arise
in Flight Deck Officer (FDO) training. RM is based on generic pattern
recognition framework. Gestures are represented as templates using
summary statistics. The proposed recognition algorithm exploits temporal
and spatial characteristics of gestures via dynamic programming
and Markovian process. The algorithm predicts corresponding index
of incremental input data in the templates in an on-line mode.
Accumulated consistency in the sequence of prediction provides a
similarity measurement (Score) between input data and the templates.
The algorithm provides an intuitive mechanism for automatic detection
of start/end frames of continuous gestures. In the present paper,
we consider isolated gestures. The performance of RM is evaluated
using four datasets - artificial (W TTest), hand motion (Yang) and
FDO (tracker, vision-based ). RM achieves comparable results which
are in agreement with other on-line and off-line algorithms such as
hidden Markov model (HMM) and dynamic time warping (DTW).
The proposed algorithm has the additional advantage of providing
timely feedback for training purposes.

XML has become a popular standard for information exchange via web. Each XML document can be presented as a rooted, ordered, labeled tree. The Node label shows the exact position of a node in the original document. Region and Dewey encoding are two famous methods of labeling trees. In this paper, we propose a new insert friendly labeling method named IFDewey based on recently proposed scheme, called Extended Dewey. In Extended Dewey many labels must be modified when a new node is inserted into the XML tree. Our method eliminates this problem by reserving even numbers for future insertion. Numbers generated by Extended Dewey may be even or odd. IFDewey modifies Extended Dewey so that only odd numbers are generated and even numbers can then be used for a much easier insertion of nodes.

On a such wide-area environment as a Grid, data
placement is an important aspect of distributed database systems. In
this paper, we address the problem of initial placement of database
no-replicated fragments in Grid architecture. We propose a graph
based approach that considers resource restrictions. The goal is to
optimize the use of computing, storage and communication
resources. The proposed approach is developed in two phases: in the
first phase, we perform fragment grouping using knowledge about
fragments dependency and, in the second phase, we determine an
efficient placement of the fragment groups on the Grid. We also
show, via experimental analysis that our approach gives solutions
that are close to being optimal for different databases and Grid
configurations.

An application of Beta wavelet networks to
synthesize pass-high and pass-low wavelet filters is investigated in
this work. A Beta wavelet network is constructed using a parametric
function called Beta function in order to resolve some nonlinear
approximation problem. We combine the filter design theory with
wavelet network approximation to synthesize perfect filter
reconstruction. The order filter is given by the number of neurons in
the hidden layer of the neural network. In this paper we use only the
first derivative of Beta function to illustrate the proposed design
procedures and exhibit its performance.

This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate Ôêºf as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.

Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Representing objects in a dynamic domain is essential
in commonsense reasoning under some circumstances. Classical logics
and their nonmonotonic consequences, however, are usually not
able to deal with reasoning with dynamic domains due to the fact that
every constant in the logical language denotes some existing object
in the static domain. In this paper, we explore a logical formalization
which allows us to represent nonexisting objects in commonsense
reasoning. A formal system named N-theory is proposed for this
purpose and its possible application in computer security is briefly
discussed.

This paper is to investigate the impplementation of security
mechanism in object oriented database system. Formal methods
plays an essential role in computer security due to its powerful expressiveness
and concise syntax and semantics. In this paper, both issues
of specification and implementation in database security environment
will be considered; and the database security is achieved through
the development of an efficient implementation of the specification
without compromising its originality and expressiveness.

In this paper, we start by first characterizing the most
important and distinguishing features of wavelet-based watermarking
schemes. We studied the overwhelming amount of algorithms
proposed in the literature. Application scenario, copyright protection
is considered and building on the experience that was gained,
implemented two distinguishing watermarking schemes. Detailed
comparison and obtained results are presented and discussed. We
concluded that Joo-s [1] technique is more robust for standard noise
attacks than Dote-s [2] technique.

Full adders are important components in applications
such as digital signal processors (DSP) architectures and
microprocessors. In addition to its main task, which is adding two
numbers, it participates in many other useful operations such as
subtraction, multiplication, division,, address calculation,..etc. In
most of these systems the adder lies in the critical path that
determines the overall speed of the system. So enhancing the
performance of the 1-bit full adder cell (the building block of the
adder) is a significant goal.Demands for the low power VLSI have
been pushing the development of aggressive design methodologies to
reduce the power consumption drastically. To meet the growing
demand, we propose a new low power adder cell by sacrificing the
MOS Transistor count that reduces the serious threshold loss
problem, considerably increases the speed and decreases the power
when compared to the static energy recovery full (SERF) adder. So a
new improved 14T CMOS l-bit full adder cell is presented in this
paper. Results show 50% improvement in threshold loss problem,
45% improvement in speed and considerable power consumption
over the SERF adder and other different types of adders with
comparable performance.

This paper investigates the problem of automated defect
detection for textile fabrics and proposes a new optimal filter design
method to solve this problem. Gabor Wavelet Network (GWN) is
chosen as the major technique to extract the texture features from
textile fabrics. Based on the features extracted, an optimal Gabor filter
can be designed. In view of this optimal filter, a new semi-supervised
defect detection scheme is proposed, which consists of one real-valued
Gabor filter and one smoothing filter. The performance of the scheme
is evaluated by using an offline test database with 78 homogeneous
textile images. The test results exhibit accurate defect detection with
low false alarm, thus showing the effectiveness and robustness of the
proposed scheme. To evaluate the detection scheme comprehensively,
a prototyped detection system is developed to conduct a real time test.
The experiment results obtained confirm the efficiency and
effectiveness of the proposed detection scheme.

In this work the opportunity of construction of the
qualifiers for face-recognition systems based on conjugation criteria
is investigated. The linkage between the bipartite conjugation, the
conjugation with a subspace and the conjugation with the null-space
is shown. The unified solving rule is investigated. It makes the
decision on the rating of face to a class considering the linkage
between conjugation values. The described recognition method can
be successfully applied to the distributed systems of video control
and video observation.

Over the past few years, a number of efforts have
been exerted to build parallel processing systems that utilize the idle
power of LAN-s and PC-s available in many homes and corporations.
The main advantage of these approaches is that they provide cheap
parallel processing environments for those who cannot afford the
expenses of supercomputers and parallel processing hardware.
However, most of the solutions provided are not very flexible in the
use of available resources and very difficult to install and setup.
In this paper, a multi-level web-based parallel processing system
(MWPS) is designed (appendix). MWPS is based on the idea of
volunteer computing, very flexible, easy to setup and easy to use.
MWPS allows three types of subscribers: simple volunteers (single
computers), super volunteers (full networks) and end users. All of
these entities are coordinated transparently through a secure web site.
Volunteer nodes provide the required processing power needed by
the system end users. There is no limit on the number of volunteer
nodes, and accordingly the system can grow indefinitely. Both
volunteer and system users must register and subscribe. Once, they
subscribe, each entity is provided with the appropriate MWPS
components. These components are very easy to install.
Super volunteer nodes are provided with special components that
make it possible to delegate some of the load to their inner nodes.
These inner nodes may also delegate some of the load to some other
lower level inner nodes .... and so on. It is the responsibility of the
parent super nodes to coordinate the delegation process and deliver
the results back to the user.
MWPS uses a simple behavior-based scheduler that takes into
consideration the current load and previous behavior of processing
nodes. Nodes that fulfill their contracts within the expected time get a
high degree of trust. Nodes that fail to satisfy their contract get a
lower degree of trust.
MWPS is based on the .NET framework and provides the minimal
level of security expected in distributed processing environments.
Users and processing nodes are fully authenticated. Communications
and messages between nodes are very secure. The system has been
implemented using C#.
MWPS may be used by any group of people or companies to
establish a parallel processing or grid environment.

This work consists of three parts. First, the alias-free
condition for the conventional two-channel quadrature mirror filter
bank is analyzed using complex arithmetic. Second, the approach
developed in the first part is applied to the complex quadrature mirror
filter bank. Accordingly, the structure is simplified and the theory is
easier to follow. Finally, a new class of complex quadrature mirror
filter banks is proposed. Interesting properties of this new structure
are also discussed.

H.264/AVC offers a considerably higher improvement
in coding efficiency compared to other compression standards such
as MPEG-2, but computational complexity is increased significantly.
In this paper, we propose selective mode decision schemes for fast
intra prediction mode selection. The objective is to reduce the
computational complexity of the H.264/AVC encoder without
significant rate-distortion performance degradation. In our proposed
schemes, the intra prediction complexity is reduced by limiting the
luma and chroma prediction modes using the directional information
of the 16×16 prediction mode. Experimental results are presented to
show that the proposed schemes reduce the complexity by up to 78%
maintaining the similar PSNR quality with about 1.46% bit rate
increase in average.

This paper focuses on the data-driven generation
of fuzzy IF...THEN rules. The resulted fuzzy rule base can be
applied to build a classifier, a model used for prediction, or
it can be applied to form a decision support system. Among
the wide range of possible approaches, the decision tree and
the association rule based algorithms are overviewed, and two
new approaches are presented based on the a priori fuzzy
clustering based partitioning of the continuous input variables.
An application study is also presented, where the developed
methods are tested on the well known Wisconsin Breast Cancer
classification problem.

In this paper, novel statistical sampling based equalization techniques and CNN based detection are proposed to increase the spectral efficiency of multiuser communication systems over fading channels. Multiuser communication combined with selective fading can result in interferences which severely deteriorate the quality of service in wireless data transmission (e.g. CDMA in mobile communication). The paper introduces new equalization methods to combat interferences by minimizing the Bit Error Rate (BER) as a function of the equalizer coefficients. This provides higher performance than the traditional Minimum Mean Square Error equalization. Since the calculation of BER as a function of the equalizer coefficients is of exponential complexity, statistical sampling methods are proposed to approximate the gradient which yields fast equalization and superior performance to the traditional algorithms. Efficient estimation of the gradient is achieved by using stratified sampling and the Li-Silvester bounds. A simple mechanism is derived to identify the dominant samples in real-time, for the sake of efficient estimation. The equalizer weights are adapted recursively by minimizing the estimated BER. The near-optimal performance of the new algorithms is also demonstrated by extensive simulations. The paper has also developed a (Cellular Neural Network) CNN based approach to detection. In this case fast quadratic optimization has been carried out by t, whereas the task of equalizer is to ensure the required template structure (sparseness) for the CNN. The performance of the method has also been analyzed by simulations.

The paper is concerned with developing stochastic delay mechanisms for efficient multicast protocols and for smooth mobile handover processes which are capable of preserving a given Quality of Service (QoS). In both applications the participating entities (receiver nodes or subscribers) sample a stochastic timer and generate load after a random delay. In this way, the load on the networking resources is evenly distributed which helps to maintain QoS communication. The optimal timer distributions have been sought in different p.d.f. families (e.g. exponential, power law and radial basis function) and the optimal parameter have been found in a recursive manner. Detailed simulations have demonstrated the improvement in performance both in the case of multicast and mobile handover applications.

Different methods containing biometric algorithms are
presented for the representation of eigenfaces detection including
face recognition, are identification and verification. Our theme of this
research is to manage the critical processing stages (accuracy, speed,
security and monitoring) of face activities with the flexibility of
searching and edit the secure authorized database. In this paper we
implement different techniques such as eigenfaces vector reduction
by using texture and shape vector phenomenon for complexity
removal, while density matching score with Face Boundary Fixation
(FBF) extracted the most likelihood characteristics in this media
processing contents. We examine the development and performance
efficiency of the database by applying our creative algorithms in both
recognition and detection phenomenon. Our results show the
performance accuracy and security gain with better achievement than
a number of previous approaches in all the above processes in an
encouraging mode.

Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.

Analytical investigation of the free vibration behavior
of circular functionally graded (FG) plates integrated with two
uniformly distributed actuator layers made of piezoelectric (PZT4)
material on the top and bottom surfaces of the circular FG plate
based on the classical plate theory (CPT) is presented in this paper.
The material properties of the functionally graded substrate plate are
assumed to be graded in the thickness direction according to the
power-law distribution in terms of the volume fractions of the
constituents and the distribution of electric potential field along the
thickness direction of piezoelectric layers is simulated by a quadratic
function. The differential equations of motion are solved analytically
for clamped edge boundary condition of the plate. The detailed
mathematical derivations are presented and Numerical investigations
are performed for FG plates with two surface-bonded piezoelectric
layers. Emphasis is placed on investigating the effect of varying the
gradient index of FG plate on the free vibration characteristics of the
structure. The results are verified by those obtained from threedimensional
finite element analyses.

Analysis for the generalized thermoelastic Lamb
waves, which propagates in anisotropic thin plates in generalized
thermoelasticity, is presented employing normal mode expansion
method. The displacement and temperature fields are expressed by a
summation of the symmetric and antisymmetric thermoelastic modes
in the surface thermal stresses and thermal gradient free orthotropic
plate, therefore the theory is particularly appropriate for waveform
analyses of Lamb waves in thin anisotropic plates. The transient
waveforms excited by the thermoelastic expansion are analyzed for
an orthotropic thin plate. The obtained results show that the theory
provides a quantitative analysis to characterize anisotropic
thermoelastic stiffness properties of plates by wave detection. Finally
numerical calculations have been presented for a NaF crystal, and the
dispersion curves for the lowest modes of the symmetric and
antisymmetric vibrations are represented graphically at different
values of thermal relaxation time. However, the methods can be used
for other materials as well

Applicability of tuning the controller gains for Stewart manipulator using genetic algorithm as an efficient search technique is investigated. Kinematics and dynamics models were introduced in detail for simulation purpose. A PD task space control scheme was used. For demonstrating technique feasibility, a Stewart manipulator numerical-model was built. A genetic algorithm was then employed to search for optimal controller gains. The controller was tested onsite a generic circular mission. The simulation results show that the technique is highly convergent with superior performance operating for different payloads.

Transesterified vegetable oils (biodiesel) are promising alternative fuel for diesel engines. Used vegetable oils are disposed from restaurants in large quantities. But higher viscosity restricts their direct use in diesel engines. In this study, used cooking oil was dehydrated and then transesterified using an alkaline catalyst. The combustion, performance and emission characteristics of Used Cooking oil Methyl Ester (UCME) and its blends with diesel oil are analysed in a direct injection C.I. engine. The fuel properties and the combustion characteristics of UCME are found to be similar to those of diesel. A minor decrease in thermal efficiency with significant improvement in reduction of particulates, carbon monoxide and unburnt hydrocarbons is observed compared to diesel. The use of transesterified used cooking oil and its blends as fuel for diesel engines will reduce dependence on fossil fuels and also decrease considerably the environmental pollution.

Over the years, there is a growing trend towards
quality-based specifications in highway construction. In many
Quality Control/Quality Assurance (QC/QA) specifications, the
contractor is primarily responsible for quality control of the process,
whereas the highway agency is responsible for testing the acceptance
of the product. A cooperative investigation was conducted in Illinois
over several years to develop a prototype End-Result Specification
(ERS) for asphalt pavement construction. The final characteristics of
the product are stipulated in the ERS and the contractor is given
considerable freedom in achieving those characteristics. The risk for
the contractor or agency depends on how the acceptance limits and
processes are specified. Stochastic simulation models are very useful
in estimating and analyzing payment risk in ERS systems and these
form an integral part of the Illinois-s prototype ERS system. This
paper describes the development of an innovative methodology to
estimate the variability components in in-situ density, air voids and
asphalt content data from ERS projects. The information gained from
this would be crucial in simulating these ERS projects for estimation
and analysis of payment risks associated with asphalt pavement
construction. However, these methods require at least two parties to
conduct tests on all the split samples obtained according to the
sampling scheme prescribed in present ERS implemented in Illinois.

In this paper, a decision aid method for preoptimization
is presented. The method is called “negotiation", and it
is based on the identification, formulation, modeling and use of
indicators defined as “negotiation indicators". These negotiation
indicators are used to explore the solution space by means of a classbased
approach. The classes are subdomains for the negotiation
indicators domain. They represent equivalent cognitive solutions in
terms of the negotiation indictors being used. By this method, we
reduced the size of the solution space and the criteria, thus aiding the
optimization methods. We present an example to show the method.

This paper describes the design concepts and
implementation of a 5-Joint mechanical arm for a rescue robot named
CEO Mission II. The multi-joint arm is a five degree of freedom
mechanical arm with a four bar linkage, which can be stretched to
125 cm. long. It is controlled by a teleoperator via the user-friendly
control and monitoring GUI program. With Inverse Kinematics
principle, we developed the method to control the servo angles of all
arm joints to get the desired tip position. By clicking the determined
tip position or dragging the tip of the mechanical arm on the
computer screen to the desired target point, the robot will compute
and move its multi-joint arm to the pose as seen on the GUI screen.
The angles of each joint are calculated and sent to all joint servos
simultaneously in order to move the mechanical arm to the desired
pose at once. The operator can also use a joystick to control the
movement of this mechanical arm and the locomotion of the robot.
Many sensors are installed at the tip of this mechanical arm for
surveillance from the high level and getting the vital signs of victims
easier and faster in the urban search and rescue tasks. It works very
effectively and easy to control. This mechanical arm and its software
were developed as a part of the CEO Mission II Rescue Robot that
won the First Runner Up award and the Best Technique award from
the Thailand Rescue Robot Championship 2006. It is a low cost,
simple, but functioning 5-Jiont mechanical arm which is built from
scratch, and controlled via wireless LAN 802.11b/g. This 5-Jiont
mechanical arm hardware concept and its software can also be used
as the basic mechatronics to many real applications.

This paper presents an efficient method of obtaining a
straight-line motion in the tool configuration space using an
articulated robot between two specified points. The simulation results
& the implementation results show the effectiveness of the method.

Analysis for the propagation of elastic waves in
arbitrary anisotropic plates is investigated, commencing with a
formal analysis of waves in a layered plate of an arbitrary anisotropic
media, the dispersion relations of elastic waves are obtained by
invoking continuity at the interface and boundary of conditions on
the surfaces of layered plate. The obtained solutions can be used for
material systems of higher symmetry such as monoclinic,
orthotropic, transversely isotropic, cubic, and isotropic as it is
contained implicitly in the analysis. The cases of free layered plate
and layered half space are considered separately. Some special cases
have also been deduced and discussed. Finally numerical solution of
the frequency equations for an aluminum epoxy is carried out, and
the dispersion curves for the few lower modes are presented. The
results obtained theoretically have been verified numerically and
illustrated graphically.

The hydrologic time series data display periodic
structure and periodic autoregressive process receives considerable
attention in modeling of such series. In this communication long
term record of monthly waste flow of Lyari river is utilized to
quantify by using PAR modeling technique. The parameters of
model are estimated by using Frances & Paap methodology. This
study shows that periodic autoregressive model of order 2 is the most
parsimonious model for assessing periodicity in waste flow of the
river. A careful statistical analysis of residuals of PAR (2) model is
used for establishing goodness of fit. The forecast by using proposed
model confirms significance and effectiveness of the model.

Axisymmetric vibration of an infinite Pyrocomposite
circular hollow cylinder made of inner and outer pyroelectric layer of
6mm-class bonded together by a Linear Elastic Material with Voids
(LEMV) layer is studied. The exact frequency equation is obtained
for the traction free surfaces with continuity condition at the
interfaces. Numerical results in the form of data and dispersion
curves for the first and second mode of the axisymmetric vibration of
the cylinder BaTio3 / Adhesive / BaTio3 by taking the Adhesive layer
as an existing Carbon Fibre Reinforced Polymer (CFRP) are
compared with a hypothetical LEMV layer with and without voids
and as well with a pyroelectric hollow cylinder. The damping is
analyzed through the imaginary parts of the complex frequencies.

This article combines two techniques: data
envelopment analysis (DEA) and Factor analysis (FA) to data
reduction in decision making units (DMU). Data envelopment
analysis (DEA), a popular linear programming technique is useful to
rate comparatively operational efficiency of decision making units
(DMU) based on their deterministic (not necessarily stochastic)
input–output data and factor analysis techniques, have been proposed
as data reduction and classification technique, which can be applied
in data envelopment analysis (DEA) technique for reduction input –
output data. Numerical results reveal that the new approach shows a
good consistency in ranking with DEA.

The objective of this study is to present the test
results of variable air volume (VAV) air conditioning system
optimized by two objective genetic algorithm (GA). The objective
functions are energy savings and thermal comfort. The optimal set
points for fuzzy logic controller (FLC) are the supply air temperature
(Ts), the supply duct static pressure (Ps), the chilled water
temperature (Tw), and zone temperature (Tz) that is taken as the
problem variables. Supply airflow rate and chilled water flow rate are
considered to be the constraints. The optimal set point values are
obtained from GA process and assigned into fuzzy logic controller
(FLC) in order to conserve energy and maintain thermal comfort in
real time VAV air conditioning system. A VAV air conditioning
system with FLC installed in a software laboratory has been taken for
the purpose of energy analysis. The total energy saving obtained in
VAV GA optimization system with FLC compared with constant air
volume (CAV) system is expected to achieve 31.5%. The optimal
duct static pressure obtained through Genetic fuzzy methodology
attributes to better air distribution by delivering the optimal quantity
of supply air to the conditioned space. This combination enhanced
the advantages of uniform air distribution, thermal comfort and
improved energy savings potential.

This paper aims at to develop a robust optimization methodology for the mechatronic modules of machine tools by considering all important characteristics from all structural and control domains in one single process. The relationship between these two domains is strongly coupled. In order to reduce the disturbance caused by parameters in either one, the mechanical and controller design domains need to be integrated. Therefore, the concurrent integrated design method Design For Control (DFC), will be employed in this paper. In this connect, it is not only applied to achieve minimal power consumption but also enhance structural performance and system response at same time. To investigate the method for integrated optimization, a mechatronic feed drive system of the machine tools is used as a design platform. Pro/Engineer and AnSys are first used to build the 3D model to analyze and design structure parameters such as elastic deformation, nature frequency and component size, based on their effects and sensitivities to the structure. In addition, the robust controller,based on Quantitative Feedback Theory (QFT), will be applied to determine proper control parameters for the controller. Therefore, overall physical properties of the machine tool will be obtained in the initial stage. Finally, the technology of design for control will be carried out to modify the structural and control parameters to achieve overall system performance. Hence, the corresponding productivity is expected to be greatly improved.

One of the major challenges in the Information
Retrieval field is handling the massive amount of information
available to Internet users. Existing ranking techniques and strategies
that govern the retrieval process fall short of expected accuracy.
Often relevant documents are buried deep in the list of documents
returned by the search engine. In order to improve retrieval accuracy
we examine the issue of language effect on the retrieval process.
Then, we propose a solution for a more biased, user-centric relevance
for retrieved data. The results demonstrate that using indices based
on variations of the same language enhances the accuracy of search
engines for individual users.

Next generation wireless/mobile networks will be IP based cellular networks integrating the internet with cellular networks. In this paper, we propose a new architecture for a high speed transport system and a mobile management protocol for mobile internet users in a transport system. Existing mobility management protocols (MIPv6, HMIPv6) do not consider real world fast moving wireless hosts (e.g. passengers in a train). For this reason, we define a virtual organization (VO) and proposed the VO architecture for the transport system. We also classify mobility as VO mobility (intra VO) and macro mobility (inter VO). Handoffs in VO are locally managed and transparent to the CH while macro mobility is managed with Mobile IPv6. And, from the features of the transport system, such as fixed route and steady speed, we deduce the movement route and the handoff disruption time of each handoff. To reduce packet loss during handoff disruption time, we propose pre-registration scheme using pre-registration. Moreover, the proposed protocol can eliminate unnecessary binding updates resulting from sequence movement at high speed. The performance evaluations demonstrate our proposed protocol has a good performance at transport system environment. Our proposed protocol can be applied to the usage of wireless internet on the train, subway, and high speed train.

This paper proposes a modeling method of the laws controlling manufacturing systems with temporal and non temporal constraints. A methodology of robust control construction generating the margins of passive and active robustness is being elaborated. Indeed, two paramount models are presented in this paper. The first utilizes the P-time Petri Nets which is used to manage the flow type disturbances. The second, the quality model, exploits the Intervals Constrained Petri Nets (ICPN) tool which allows the system to preserve its quality specificities. The redundancy of the robustness of the elementary parameters between passive and active is also used. The final model built allows the correlation of temporal and non temporal criteria by putting two paramount models in interaction. To do so, a set of definitions and theorems are employed and affirmed by applicator examples.

Recently, the Field Programmable Gate Array (FPGA) technology offers the potential of designing high performance systems at low cost. The discrete wavelet transform has gained the reputation of being a very effective signal analysis tool for many practical applications. However, due to its computation-intensive nature, current implementation of the transform falls short of meeting real-time processing requirements of most application. The objectives of this paper are implement the Haar and Daubechies wavelets using FPGA technology. In addition, the Bit Error Rate (BER) between the input audio signal and the reconstructed output signal for each wavelet is calculated. From the BER, it is seen that the implementations execute the operation of the wavelet transform correctly and satisfying the perfect reconstruction conditions. The design procedure has been explained and designed using the stat-ofart Electronic Design Automation (EDA) tools for system design on FPGA. Simulation, synthesis and implementation on the FPGA target technology has been carried out.

Response surface methodology was used for
quantitative investigation of water and solids transfer during osmotic
dehydration of beetroot in aqueous solution of salt. Effects of
temperature (25 – 45oC), processing time (30–150 min), salt
concentration (5–25%, w/w) and solution to sample ratio (5:1 – 25:1)
on osmotic dehydration of beetroot were estimated. Quadratic
regression equations describing the effects of these factors on the
water loss and solids gain were developed. It was found that effects
of temperature and salt concentrations were more significant on the
water loss than the effects of processing time and solution to sample
ratio. As for solids gain processing time and salt concentration were
the most significant factors. The osmotic dehydration process was
optimized for water loss, solute gain, and weight reduction. The
optimum conditions were found to be: temperature – 35oC,
processing time – 90 min, salt concentration – 14.31% and solution
to sample ratio 8.5:1. At these optimum values, water loss, solid gain
and weight reduction were found to be 30.86 (g/100 g initial sample),
9.43 (g/100 g initial sample) and 21.43 (g/100 g initial sample)
respectively.

Laboratory experiments have been performed to investigate photocatalytic detoxification by using TiO2 photocatalyst for treating dairy effluent. Various operational parameters such as catalyst concentration, initial concentration, angle of tilt of solar flat plate reactor and flow rate were investigated. Results indicated that the photocatalytic detoxification process can efficiently treat dairy effluent. Experimental runs with dairy wastewater can be used to identify the optimum operational parameters to perform wastewater degradation on large scale for recycling purpose. Also effect of two different types of reactors on degradation process was analyzed.

Enzymatic hydrolysis of starch from natural sources
finds potential application in commercial production of alcoholic
beverage and bioethanol. In this study the effect of starch
concentration, temperature, time and enzyme concentration were
studied and optimized for hydrolysis of cassava (Manihot esculenta)
starch powder (of mesh 80/120) into glucose syrup by immobilized
(using Polyacrylamide gel) a-amylase using central composite
design. The experimental result on enzymatic hydrolysis of cassava
starch was subjected to multiple linear regression analysis using
MINITAB 14 software. Positive linear effect of starch concentration,
enzyme concentration and time was observed on hydrolysis of
cassava starch by a-amylase. The statistical significance of the model
was validated by F-test for analysis of variance (p < 0.01). The
optimum value of starch concentration temperature, time and enzyme
concentration were found to be 4.5% (w/v), 45oC, 150 min, and 1%
(w/v) enzyme. The maximum glucose yield at optimum condition
was 5.17 mg/mL.

A predictive clustering hybrid regression (pCHR)
approach was developed and evaluated using dataset from H2-
producing sucrose-based bioreactor operated for 15 months. The aim
was to model and predict the H2-production rate using information
available about envirome and metabolome of the bioprocess. Selforganizing
maps (SOM) and Sammon map were used to visualize the
dataset and to identify main metabolic patterns and clusters in
bioprocess data. Three metabolic clusters: acetate coupled with other
metabolites, butyrate only, and transition phases were detected. The
developed pCHR model combines principles of k-means clustering,
kNN classification and regression techniques. The model performed
well in modeling and predicting the H2-production rate with mean
square error values of 0.0014 and 0.0032, respectively.

Group contribution based models are widely used in
industrial applications for its convenience and flexibility. Although a
number of group contribution models have been proposed, there were
certain limitations inherent to those models. Models based on group
contribution excess Gibbs free energy are limited to low pressures and
models based on equation of state (EOS) cannot properly describe
highly nonideal mixtures including acids without introducing
additional modification such as chemical theory. In the present study
new a new approach derived from quantum chemistry have been used
to calculate necessary EOS group interaction parameters. The
COSMO-RS method, based on quantum mechanics, provides a
reliable tool for fluid phase thermodynamics. Benefits of the group
contribution EOS are the consistent extension to hydrogen-bonded
mixtures and the capability to predict polymer-solvent equilibria up to
high pressures. The authors are confident that with a sufficient
parameter matrix the performance of the lattice EOS can be improved
significantly.

This paper proposes a new technique for improving
the efficiency of software testing, which is based on a conventional
attempt to reduce test cases that have to be tested for any given
software. The approach utilizes the advantage of Regression Testing
where fewer test cases would lessen time consumption of the testing
as a whole. The technique also offers a means to perform test case
generation automatically. Compared to one of the techniques in the
literature where the tester has no option but to perform the test case
generation manually, the proposed technique provides a better
option. As for the test cases reduction, the technique uses simple
algebraic conditions to assign fixed values to variables (Maximum,
minimum and constant variables). By doing this, the variables values
would be limited within a definite range, resulting in fewer numbers
of possible test cases to process. The technique can also be used in
program loops and arrays.

Induction machine models used for steady-state and
transient analysis require machine parameters that are usually
considered design parameters or data. The knowledge of induction
machine parameters is very important for Indirect Field Oriented
Control (IFOC). A mismatched set of parameters will degrade the
response of speed and torque control. This paper presents an
improvement approach on rotor time constant adaptation in IFOC for
Induction Machines (IM). Our approach tends to improve the
estimation accuracy of the fundamental model for flux estimation.
Based on the reduced order of the IM model, the rotor fluxes and
rotor time constant are estimated using only the stator currents and
voltages. This reduced order model offers many advantages for real
time identification parameters of the IM.

In this paper, the decomposition-aggregation method
is used to carry out connective stability criteria for general linear
composite system via aggregation. The large scale system is
decomposed into a number of subsystems. By associating directed
graphs with dynamic systems in an essential way, we define the
relation between system structure and stability in the sense of
Lyapunov. The stability criteria is then associated with the stability
and system matrices of subsystems as well as those interconnected
terms among subsystems using the concepts of vector differential
inequalities and vector Lyapunov functions. Then, we show that the
stability of each subsystem and stability of the aggregate model
imply connective stability of the overall system. An example is
reported, showing the efficiency of the proposed technique.

Analytical expression for maximum power transfer
through a transmission line limited by voltage stability has been
formulated using exact representation of transmission line with
ABCD parameters. The expression has been used for plotting PV
curve at different power factors of a radial transmission line.
Limiting values of reactive power have been obtained.

In real-field applications, the correct determination of voice segments highly improves the overall system accuracy and minimises the total computation time. This paper presents reliable measures of speech compression by detcting the end points of the speech signals prior to compressing them. The two different compession schemes used are the Global threshold and the Level- Dependent threshold techniques. The performance of the proposed method is tested wirh the Signal to Noise Ratios, Peak Signal to Noise Ratios and Normalized Root Mean Square Error parameter measures.

Positioning the organization in the strategic
environment of its industry is one of the first and most important
phases of the organizational strategic planning and in today
knowledge-based economy has its importance been duplicated for
higher education institutes as the centers of education, knowledge
creation and knowledge worker training. Up to now, various models
with diverse approaches have been applied to investigate
organizations- strategic position in different industries. Regarding the
essential importance and strategic role of quality in higher education
institutes, in this study, a quality-oriented approach has been
suggested to positioning them in their strategic environment. Then
the European Foundation of Quality Management (EFQM) model has
been adopted to position the top Iranian business schools in their
strategic environment. The result of this study can be used in strategic
planning of these institutes as well as the other Iranian business
schools.

Defining strategic position of the organizations within
the industry environment is one of the basic and most important
phases of strategic planning to which extent that one of the
fundamental schools of strategic planning is the strategic positioning
school. In today-s knowledge-based economy and dynamic
environment, it is essential for universities as the centers of
education, knowledge creation and knowledge worker evolvement.
Till now, variant models with different approaches to strategic
positioning are deployed in defining the strategic position within the
various industries. Balanced Scorecard as one of the powerful models
for strategic positioning, analyzes all aspects of the organization
evenly. In this paper with the consideration of BSC strength in
strategic evaluation, it is used for analyzing the environmental
position of the best-s Iranian Business Schools. The results could be
used in developing strategic plans for these schools as well as other
Iranian Management and Business Schools.

Fractional Fourier Transform is a generalization of the
classical Fourier Transform. The Fractional Fourier span in general
depends on the amplitude and phase functions of the signal and varies
with the transform order. However, with the development of the
Fractional Fourier filter banks, it is advantageous in some cases to
have different transform orders for different filter banks to achieve
better decorrelation of the windowed and overlapped time signal. We
present an expression that is useful for finding the perturbation in the
Fractional Fourier span due to the erroneous transform order and the
possible variation in the window shape and length. The expression is
based on the dependency of the time-Fractional Fourier span
Uncertainty on the amplitude and phase function of the signal. We
also show with the help of the developed expression that the
perturbation of span has a varying degree of sensitivity for varying
degree of transform order and the window coefficients.

Recognizing the increasing importance of using the
Internet to conduct business, this paper looks at some related matters
associated with small businesses making a decision of whether or not
to have a Website and go online. Small businesses in Saudi Arabia
struggle to have this decision. For organizations, to fully go online,
conduct business and provide online information services, they need
to connect their database to the Web. Some issues related to doing
that might be beyond the capabilities of most small businesses in
Saudi Arabia, such as Website management, technical issues and
security concerns. Here we focus on a small business firm in Saudi
Arabia (Case Study), discussing the issues related to going online
decision and the firm's options of what to do and how to do it. The
paper suggested some valuable solutions of connecting databases to
the Web. It also discusses some of the important issues related to
online information services and e-commerce, mainly Web hosting
options and security issues.

The energy consumption and delay in read/write
operation of conventional SRAM is investigated analytically as well
as by simulation. Explicit analytical expressions for the energy
consumption and delay in read and write operation as a function of
device parameters and supply voltage are derived. The expressions are
useful in predicting the effect of parameter changes on the energy
consumption and speed as well as in optimizing the design of
conventional SRAM. HSPICE simulation in standard 0.25μm CMOS
technology confirms precision of analytical expressions derived from
this paper.

The special constraints of sensor networks impose a
number of technical challenges for employing them. In this review,
we study the issues and existing protocols in three areas: coverage
and routing. We present two types of coverage problems: to
determine the minimum number of sensor nodes that need to perform
active sensing in order to monitor a certain area; and to decide the
quality of service that can be provided by a given sensor network.
While most routing protocols in sensor networks are data-centric,
there are other types of routing protocols as well, such as
hierarchical, location-based, and QoS-aware. We describe and
compare several protocols in each group. We present several multipath
routing protocols and single-path with local repair routing
protocols, which are proposed for recovering from sensor node
crashes. We also discuss some transport layer schemes for reliable
data transmission in lossy wireless channels.

With the rapid growth in business size, today's businesses orient towards electronic technologies. Amazon.com and e-bay.com are some of the major stakeholders in this regard. Unfortunately the enormous size and hugely unstructured data on the web, even for a single commodity, has become a cause of ambiguity for consumers. Extracting valuable information from such an everincreasing data is an extremely tedious task and is fast becoming critical towards the success of businesses. Web content mining can play a major role in solving these issues. It involves using efficient algorithmic techniques to search and retrieve the desired information from a seemingly impossible to search unstructured data on the Internet. Application of web content mining can be very encouraging in the areas of Customer Relations Modeling, billing records, logistics investigations, product cataloguing and quality management. In this paper we present a review of some very interesting, efficient yet implementable techniques from the field of web content mining and study their impact in the area specific to business user needs focusing both on the customer as well as the producer. The techniques we would be reviewing include, mining by developing a knowledge-base repository of the domain, iterative refinement of user queries for personalized search, using a graphbased approach for the development of a web-crawler and filtering information for personalized search using website captions. These techniques have been analyzed and compared on the basis of their execution time and relevance of the result they produced against a particular search.

Nowadays, the pace of business change is such that,
increasingly, new functionality has to be realized and reliably
installed in a matter of days, or even hours. Consequently, more and
more business processes are prone to a continuous change. The
objective of the research in progress is to use the MAP model, in a
conceptual modeling method for flexible and adaptive business
process. This method can be used to capture the flexibility
dimensions of a business process; it takes inspiration from
modularity concept in the object oriented paradigm to establish a
hierarchical construction of the BP modeling. Its intent is to provide
a flexible modeling that allows companies to quickly adapt their
business processes.

Fast retrieval of data has been a need of user in any
database application. This paper introduces a buffer based query
optimization technique in which queries are assigned weights
according to their number of execution in a query bank. These
queries and their optimized executed plans are loaded into the buffer
at the start of the database application. For every query the system
searches for a match in the buffer and executes the plan without
creating new plans.

Expert systems are used extensively in many domains.
This paper discusses the use of medical expert systems in Pakistan.
Countries all over the world pay special attention on health facilities.
A country like Pakistan faces a lot of trouble in health sector.
Several attempts have been made in Pakistan to improve the health
conditions of the people but the situation is still not encouraging.
There is a shortage of doctors and other trained personnel in
Pakistan. Expert systems can play a vital role in such cases where the
medical expert is not readily available. The purpose of this paper is
to analyze the role that such systems can play in improving the health
conditions of the people in Pakistan.

With the rapid growth in business size, today-s businesses orient Throughout thirty years local, national and international experience in medicine as a medical student, junior doctor and eventually Consultant and Professor in Anaesthesia, Intensive Care and Pain Management, I note significant generalised dissatisfaction among medical students and doctors regarding their medical education and practice. We repeatedly hear complaints from patients about the dysfunctional health care system they are dealing with and subsequently the poor medical service that they are receiving. Medical students are bombarded with lectures, tutorials, clinical rounds and various exams. Clinicians are weighed down with a never-ending array of competing duties. Patients are extremely unhappy about the long waiting lists, loss of their records and the continuous deterioration of the health care service. This problem has been reported in different countries by several authors [1,2,3]. In a trial to solve this dilemma, a genuine idea has been suggested implementing computer technology in medicine [2,3]. Computers in medicine are a medium of international communication of the revolutionary advances being made in the application of the computer to the fields of bioscience and medicine [4,5]. The awareness about using computers in medicine has recently increased all over the world. In Misr University for Science & Technology (MUST), Egypt, medical students are now given hand-held computers (Laptop) with Internet facility making their medical education accessible, convenient and up to date. However, this trial still needs to be validated. Helping the readers to catch up with the on going fast development in this interesting field, the author has decided to continue reviewing the literature, exploring the state-of-art in computer based medicine and up dating the medical professionals especially the local trainee Doctors in Egypt. In part I of this review article we will give a general background discussing the potential use of computer technology in the various aspects of the medical field including education, research, clinical practice and the health care service given to patients. Hope this will help starting changing the culture, promoting the awareness about the importance of implementing information technology (IT) in medicine, which is a field in which such help is needed. An international collaboration is recommended supporting the emerging countries achieving this target.

In this paper a PID control strategy using neural
network adaptive RASP1 wavelet for WECS-s control is proposed.
It is based on single layer feedforward neural networks with hidden
nodes of adaptive RASP1 wavelet functions controller and an infinite
impulse response (IIR) recurrent structure. The IIR is combined by
cascading to the network to provide double local structure resulting
in improving speed of learning. This particular neuro PID controller
assumes a certain model structure to approximately identify the
system dynamics of the unknown plant (WECS-s) and generate the
control signal. The results are applied to a typical turbine/generator
pair, showing the feasibility of the proposed solution.

Due to the increasing penetration of wind energy, it is
necessary to possess design tools that are able to simulate the impact
of these installations in utility grids. In order to provide a net
contribution to this issue a detailed wind park model has been
developed and is briefly presented. However, the computational costs
associated with the performance of such a detailed model in
describing the behavior of a wind park composed by a considerable
number of units may render its practical application very difficult. To
overcome this problem integral manifolds theory has been applied to
reduce the order of the detailed wind park model, and therefore
create the conditions for the development of a dynamic equivalent
which is able to retain the relevant dynamics with respect to the
existing a.c. system. In this paper integral manifold method has been
introduced for order reduction. Simulation results of the proposed
method represents that integral manifold method results fit the
detailed model results with a higher precision than singular
perturbation method.

A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.

This paper presents a method for the optimal
allocation of Distributed generation in distribution systems. In this
paper, our aim would be optimal distributed generation allocation for
voltage profile improvement and loss reduction in distribution
network. Genetic Algorithm (GA) was used as the solving tool,
which referring two determined aim; the problem is defined and
objective function is introduced. Considering to fitness values
sensitivity in genetic algorithm process, there is needed to apply load
flow for decision-making. Load flow algorithm is combined
appropriately with GA, till access to acceptable results of this
operation. We used MATPOWER package for load flow algorithm
and composed it with our Genetic Algorithm. The suggested method
is programmed under MATLAB software and applied ETAP
software for evaluating of results correctness. It was implemented on
part of Tehran electricity distributing grid. The resulting operation of
this method on some testing system is illuminated improvement of
voltage profile and loss reduction indexes.

Wind turbines with double output induction
generators can operate at variable speed permitting conversion
efficiency maximization over a wide range of wind velocities. This
paper presents the performance analysis of a wind driven double
output induction generator (DOIG) operating at varying shafts speed.
A periodic transient state analysis of DOIG equipped with two
converters is carried out using a hybrid induction machine model.
This paper simulates the harmonic content of waveforms in various
points of drive at different speeds, based on the hybrid model
(dqabc). Then the sinusoidal and trapezoidal pulse-width–modulation
control techniques are used in order to improve the power factor of
the machine and to weaken the injected low order harmonics to the
supply. Based on the frequency spectrum, total harmonics distortion,
distortion factor and power factor. Finally advantages of sinusoidal
and trapezoidal pulse width modulation techniques are compared.

A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.

The vast amount of information hidden in huge
databases has created tremendous interests in the field of data
mining. This paper examines the possibility of using data clustering
techniques in oral medicine to identify functional relationships
between different attributes and classification of similar patient
examinations. Commonly used data clustering algorithms have been
reviewed and as a result several interesting results have been
gathered.

Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.

Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.

In order to accelerate the similarity search in highdimensional database, we propose a new hierarchical indexing method. It is composed of offline and online phases. Our contribution concerns both phases. In the offline phase, after gathering the whole of the data in clusters and constructing a hierarchical index, the main originality of our contribution consists to develop a method to construct bounding forms of clusters to avoid overlapping. For the online phase, our idea improves considerably performances of similarity search. However, for this second phase, we have also developed an adapted search algorithm. Our method baptized NOHIS (Non-Overlapping Hierarchical Index Structure) use the Principal Direction Divisive Partitioning (PDDP) as algorithm of clustering. The principle of the PDDP is to divide data recursively into two sub-clusters; division is done by using the hyper-plane orthogonal to the principal direction derived from the covariance matrix and passing through the centroid of the cluster to divide. Data of each two sub-clusters obtained are including by a minimum bounding rectangle (MBR). The two MBRs are directed according to the principal direction. Consequently, the nonoverlapping between the two forms is assured. Experiments use databases containing image descriptors. Results show that the proposed method outperforms sequential scan and SRtree in processing k-nearest neighbors.

From a set of shifted, blurred, and decimated image , super-resolution image reconstruction can get a high-resolution image. So it has become an active research branch in the field of image restoration. In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization methods. In the regularization methods at present, however, regularization parameter was selected by experience in some cases and other techniques have too heavy computation cost for computing the parameter. In this paper, we construct a new super-resolution algorithm by transforming the solving of the System stem Є=An into the solving of the equations X+A*X-1A=I , and propose an inverse iterative method.

In this paper, various algorithms for designing quadrature mirror filter are reviewed and a new algorithm is presented for the design of near perfect reconstruction quadrature mirror filter bank. In the proposed algorithm, objective function is formulated using the perfect reconstruction condition or magnitude response condition of prototype filter at frequency (ω = 0.5π) in ideal condition. The cutoff frequency is iteratively changed to adjust the filters coefficients using optimization algorithm. The performances of the proposed algorithm are evaluated in term of computation time, reconstruction error and number of iterations. The design examples illustrate that the proposed algorithm is superior in term of peak reconstruction error, computation time, and number of iterations. The proposed algorithm is simple, easy to implement, and linear in nature.

The production of a plant can be measured in terms of
seeds. The generation of seeds plays a critical role in our social and
daily life. The fruit production which generates seeds, depends on the
various parameters of the plant, such as shoot length, leaf number,
root length, root number, etc When the plant is growing, some leaves
may be lost and some new leaves may appear. It is very difficult to
use the number of leaves of the tree to calculate the growth of the
plant.. It is also cumbersome to measure the number of roots and
length of growth of root in several time instances continuously after
certain initial period of time, because roots grow deeper and deeper
under ground in course of time. On the contrary, the shoot length of
the tree grows in course of time which can be measured in different
time instances. So the growth of the plant can be measured using the
data of shoot length which are measured at different time instances
after plantation. The environmental parameters like temperature, rain
fall, humidity and pollution are also play some role in production of
yield. The soil, crop and distance management are taken care to
produce maximum amount of yields of plant. The data of the growth
of shoot length of some mustard plant at the initial stage (7,14,21 &
28 days after plantation) is available from the statistical survey by a
group of scientists under the supervision of Prof. Dilip De. In this
paper, initial shoot length of Ken( one type of mustard plant) has
been used as an initial data. The statistical models, the methods of
fuzzy logic and neural network have been tested on this mustard
plant and based on error analysis (calculation of average error) that
model with minimum error has been selected and can be used for the
assessment of shoot length at maturity. Finally, all these methods
have been tested with other type of mustard plants and the particular
soft computing model with the minimum error of all types has been
selected for calculating the predicted data of growth of shoot length.
The shoot length at the stage of maturity of all types of mustard
plants has been calculated using the statistical method on the
predicted data of shoot length.

This work proposes an approach to address automatic
text summarization. This approach is a trainable summarizer, which
takes into account several features, including sentence position,
positive keyword, negative keyword, sentence centrality, sentence
resemblance to the title, sentence inclusion of name entity, sentence
inclusion of numerical data, sentence relative length, Bushy path of
the sentence and aggregated similarity for each sentence to generate
summaries. First we investigate the effect of each sentence feature on
the summarization task. Then we use all features score function to
train genetic algorithm (GA) and mathematical regression (MR)
models to obtain a suitable combination of feature weights. The
proposed approach performance is measured at several compression
rates on a data corpus composed of 100 English religious articles.
The results of the proposed approach are promising.

This paper presents a perturbation based search method
to solve the unconstrained binary quadratic programming problem.
The proposed algorithm was tested with some of the standard test
problems and the results are reported for 10 instances of 50, 100, 250,
& 500 variable problems. A comparison of the performance of the
proposed algorithm with other heuristics and optimization software is
made. Based on the results, it was found that the proposed algorithm
is computationally inexpensive and the solutions obtained match the
best known solutions for smaller sized problems. For larger instances,
the algorithm is capable of finding a solution within 0.11% of the
best known solution. Apart from being used as a stand-alone method,
this algorithm could also be incorporated with other heuristics to find
better solutions.

In this paper, SFQ (Start Time Fair Queuing)
algorithm is analyzed when this is applied in computer networks to
know what kind of behavior the traffic in the net has when different
data sources are managed by the scheduler. Using the NS2 software
the computer networks were simulated to be able to get the graphs
showing the performance of the scheduler. Different traffic sources
were introduced in the scripts, trying to establish the real scenario.
Finally the results were that depending on the data source, the traffic
can be affected in different levels, when Constant Bite Rate is
applied, the scheduler ensures a constant level of data sent and
received, but the truth is that in the real life it is impossible to ensure
a level that resists the changes in work load.

Rounding of coefficients is a common practice in
hardware implementation of digital filters. Where some coefficients
are very close to zero or one, as assumed in this paper, this rounding
action also leads to some computation reduction. Furthermore, if the
discarded coefficient is of high order, a reduced order filter is
obtained, otherwise the order does not change but computation is
reduced. In this paper, the Least Squares approximation to rounded
(or discarded) coefficient FIR filter is investigated. The result also
succinctly extended to general type of FIR filters.

Artifact free photoplethysmographic (PPG) signals are
necessary for non-invasive estimation of oxygen saturation (SpO2) in
arterial blood. Movement of a patient corrupts the PPGs with motion
artifacts, resulting in large errors in the computation of Sp02. This
paper presents a study on using Kalman Filter in an innovative way
by modeling both the Artillery Blood Pressure (ABP) and the
unwanted signal, additive motion artifact, to reduce motion artifacts
from corrupted PPG signals. Simulation results show acceptable
performance regarding LMS and variable step LMS, thus
establishing the efficacy of the proposed method.

This paper introduces the effective speckle reduction of
synthetic aperture radar (SAR) images using inner product spaces in
undecimated wavelet domain. There are two major areas in projection
onto span algorithm where improvement can be made. First is the use
of undecimated wavelet transformation instead of discrete wavelet
transformation. And second area is the use of smoothing filter namely
directional smoothing filter which is an additional step. Proposed
method does not need any noise estimation and thresholding
technique. More over proposed method gives good results on both
single polarimetric and fully polarimetric SAR images.

Generally, in order to create 3D sound using binaural
systems, we use head related transfer functions (HRTF) including the
information of sounds which is arrived to our ears. But it can decline
some three-dimensional effects in the area of a cone of confusion
between front and back directions, because of the characteristics of
HRTF.
In this paper, we propose a new method to use psychoacoustics
theory that reduces the confusion of sound image localization. In the
method, HRTF spectrum characteristic is enhanced by using the
energy ratio of the bark band. Informal listening tests show that the
proposed method improves the front-back sound localization
characteristics much better than the conventional methods

In this paper, application of artificial neural networks
in typical disease diagnosis has been investigated. The real procedure
of medical diagnosis which usually is employed by physicians was
analyzed and converted to a machine implementable format. Then
after selecting some symptoms of eight different diseases, a data set
contains the information of a few hundreds cases was configured and
applied to a MLP neural network. The results of the experiments and
also the advantages of using a fuzzy approach were discussed as
well. Outcomes suggest the role of effective symptoms selection and
the advantages of data fuzzificaton on a neural networks-based
automatic medical diagnosis system.

This paper describes the project and development of a
very low-cost and small electronic prototype, especially designed for
monitoring and controlling existing home automation alarm systems
(intruder, smoke, gas, flood, etc.), via TCP/IP, with a typical web
browser. Its use will allow home owners to be immediately alerted
and aware when an alarm event occurs, and being also able to
interact with their home automation alarm system, disarming, arming
and watching event alerts, with a personal wireless Wi-Fi PDA or
smartphone logged on to a dedicated predefined web page, and using
also a PC or Laptop.

Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.

We present an Electronic Nose (ENose), which is
aimed at identifying the presence of one out of two gases, possibly
detecting the presence of a mixture of the two. Estimation of the
concentrations of the components is also performed for a volatile
organic compound (VOC) constituted by methanol and acetone, for
the ranges 40-400 and 22-220 ppm (parts-per-million), respectively.
Our system contains 8 sensors, 5 of them being gas sensors (of the
class TGS from FIGARO USA, INC., whose sensing element is a tin
dioxide (SnO2) semiconductor), the remaining being a temperature
sensor (LM35 from National Semiconductor Corporation), a
humidity sensor (HIH–3610 from Honeywell), and a pressure sensor
(XFAM from Fujikura Ltd.).
Our integrated hardware–software system uses some machine
learning principles and least square regression principle to identify at
first a new gas sample, or a mixture, and then to estimate the
concentrations. In particular we adopt a training model using the
Support Vector Machine (SVM) approach with linear kernel to teach
the system how discriminate among different gases. Then we apply
another training model using the least square regression, to predict
the concentrations.
The experimental results demonstrate that the proposed
multiclassification and regression scheme is effective in the
identification of the tested VOCs of methanol and acetone with
96.61% correctness. The concentration prediction is obtained with
0.979 and 0.964 correlation coefficient for the predicted versus real
concentrations of methanol and acetone, respectively.

Hypernetworks are a generalized graph structure
representing higher-order interactions between variables. We present a
method for self-organizing hypernetworks to learn an associative
memory of sentences and to recall the sentences from this memory.
This learning method is inspired by the “mental chemistry" model of
cognition and the “molecular self-assembly" technology in
biochemistry. Simulation experiments are performed on a corpus of
natural-language dialogues of approximately 300K sentences
collected from TV drama captions. We report on the sentence
completion performance as a function of the order of word-interaction
and the size of the learning corpus, and discuss the plausibility of this
architecture as a cognitive model of language learning and memory.

Information Retrieval has the objective of studying
models and the realization of systems allowing a user to find the
relevant documents adapted to his need of information. The
information search is a problem which remains difficult because the
difficulty in the representing and to treat the natural languages such
as polysemia. Intentional Structures promise to be a new paradigm to
extend the existing documents structures and to enhance the different
phases of documents process such as creation, editing, search and
retrieval. The intention recognition of the author-s of texts can reduce
the largeness of this problem. In this article, we present intentions
recognition system is based on a semi-automatic method of
extraction the intentional information starting from a corpus of text.
This system is also able to update the ontology of intentions for the
enrichment of the knowledge base containing all possible intentions
of a domain. This approach uses the construction of a semi-formal
ontology which considered as the conceptualization of the intentional
information contained in a text. An experiments on scientific
publications in the field of computer science was considered to
validate this approach.

An ontology is widely used in many kinds of applications as a knowledge representation tool for domain knowledge. However, even though an ontology schema is well prepared by domain experts, it is tedious and cost-intensive to add instances into the ontology. The most confident and trust-worthy way to add instances into the ontology is to gather instances from tables in the related Web pages. In automatic populating of instances, the primary task is to find the most proper concept among all possible concepts within the ontology for a given table. This paper proposes a novel method for this problem by defining the similarity between the table and the concept using the overlap of their properties. According to a series of experiments, the proposed method achieves 76.98% of accuracy. This implies that the proposed method is a plausible way for automatic ontology population from Web tables.

A number of previous studies were rarely considered
the effects of transient non-uniform balloon expansion on evaluation
of the properties and behaviors of stents during stent expansion, nor
did they determine parameters to maximize the performances driven
by mechanical characteristics. Therefore, in order to fully understand
the mechanical characteristics and behaviors of stent, it is necessary to
consider a realistic modeling of transient non-uniform balloon-stent
expansion. The aim of the study is to propose design parameters
capable of improving the ability of vascular stent through a
comparative study of seven commercial stents using finite element
analyses of a realistic transient non-uniform balloon-stent expansion
process. In this study, seven representative commercialized stents were
evaluated by finite element (FE) analysis in terms of the criteria based
on the itemized list of Food and Drug Administration (FDA) and
European Standards (prEN). The results indicate that using stents
composed of opened unit cells connected by bend-shaped link
structures and controlling the geometrical and morphological features
of the unit cell strut or the link structure at the distal ends of stent may
improve mechanical characteristics of stent. This study provides a
better method at the realistic transient non-uniform balloon-stent
expansion by investigating the characteristics, behaviors, and
parameters capable of improving the ability of vascular stent.

In this paper, a study on the applications of the
optimization and regression techniques for optimal calculation of
partial ratios of helical gearboxes with second-step double gear-sets
for minimal cross section dimension is introduced. From the condition
of the moment equilibrium of a mechanic system including three gear
units and their regular resistance condition, models for calculation of
the partial ratios of helical gearboxes with second-step double
gear-sets were given. Especially, by regression analysis, explicit
models for calculation of the partial ratios are introduced. These
models allow determining the partial ratios accurately and simply.

This paper presents a new study on the applications of
optimization and regression analysis techniques for optimal
calculation of partial ratios of four-step helical gearboxes for getting
minimal gearbox length. In the paper, basing on the moment
equilibrium condition of a mechanic system including four gear units
and their regular resistance condition, models for determination of the
partial ratios of the gearboxes are proposed. In particular, explicit
models for calculation of the partial ratios are proposed by using
regression analysis. Using these models, the determination of the
partial ratios is accurate and simple.

Realistic 3D face model is more precise in representing
pose, illumination, and expression of face than 2D face model so that it
can be utilized usefully in various applications such as face recognition,
games, avatars, animations, and etc.
In this paper, we propose a 3D face modeling method based on 3D
dense morphable shape model. The proposed 3D modeling method
first constructs a 3D dense morphable shape model from 3D face scan
data obtained using a 3D scanner. Next, the proposed method extracts
and matches facial landmarks from 2D image sequence containing a
face to be modeled, and then reconstructs 3D vertices coordinates of
the landmarks using a factorization-based SfM technique. Then, the
proposed method obtains a 3D dense shape model of the face to be
modeled by fitting the constructed 3D dense morphable shape model
into the reconstructed 3D vertices. Also, the proposed method makes a
cylindrical texture map using 2D face image sequence. Finally, the
proposed method generates a 3D face model by rendering the 3D dense
face shape model using the cylindrical texture map. Through building
processes of 3D face model by the proposed method, it is shown that
the proposed method is relatively easy, fast and precise.

SAD (Sum of Absolute Difference) algorithm is
heavily used in motion estimation which is computationally highly
demanding process in motion picture encoding. To enhance the
performance of motion picture encoding on a VLIW processor, an
efficient implementation of SAD algorithm on the VLIW processor is
essential. SAD algorithm is programmed as a nested loop with a
conditional branch. In VLIW processors, loop is usually optimized by
software pipelining, but researches on optimal scheduling of software
pipelining for nested loops, especially nested loops with conditional
branches are rare. In this paper, we propose an optimal scheduling and
implementation of SAD algorithm with conditional branch on a VLIW
DSP processor. The proposed optimal scheduling first transforms the
nested loop with conditional branch into a single loop with conditional
branch with consideration of full utilization of ILP capability of the
VLIW processor and realization of earlier escape from the loop. Next,
the proposed optimal scheduling applies a modulo scheduling
technique developed for single loop. Based on this optimal scheduling
strategy, optimal implementation of SAD algorithm on TMS320C67x,
a VLIW DSP is presented. Through experiments on TMS320C6713
DSK, it is shown that H.263 encoder with the proposed SAD
implementation performs better than other H.263 encoder with other
SAD implementations, and that the code size of the optimal SAD
implementation is small enough to be appropriate for embedded
environments.

Robust face recognition under various illumination
environments is very difficult and needs to be accomplished for
successful commercialization. In this paper, we propose an improved
illumination normalization method for face recognition. Illumination
normalization algorithm based on anisotropic smoothing is well known
to be effective among illumination normalization methods but
deteriorates the intensity contrast of the original image, and incurs less
sharp edges. The proposed method in this paper improves the previous
anisotropic smoothing-based illumination normalization method so
that it increases the intensity contrast and enhances the edges while
diminishing the effect of illumination variations. Due to the result of
these improvements, face images preprocessed by the proposed
illumination normalization method becomes to have more distinctive
feature vectors (Gabor feature vectors) for face recognition. Through
experiments of face recognition based on Gabor feature vector
similarity, the effectiveness of the proposed illumination
normalization method is verified.

Video streaming over lossy IP networks is very
important issues, due to the heterogeneous structure of networks.
Infrastructure of the Internet exhibits variable bandwidths, delays,
congestions and time-varying packet losses. Because of variable
attributes of the Internet, video streaming applications should not
only have a good end-to-end transport performance but also have a
robust rate control, furthermore multipath rate allocation mechanism.
So for providing the video streaming service quality, some other
components such as Bandwidth Estimation and Adaptive Rate
Controller should be taken into consideration. This paper gives an
overview of video streaming concept and bandwidth estimation tools
and then introduces special architectures for bandwidth adaptive
video streaming. A bandwidth estimation algorithm – pathChirp,
Optimized Rate Controllers and Multipath Rate Allocation Algorithm
are considered as all-in-one solution for video streaming problem.
This solution is directed and optimized by a decision center which is
designed for obtaining the maximum quality at the receiving side.

This paper explores the scalability issues associated
with solving the Named Entity Recognition (NER) problem using
Support Vector Machines (SVM) and high-dimensional features. The
performance results of a set of experiments conducted using binary
and multi-class SVM with increasing training data sizes are
examined. The NER domain chosen for these experiments is the
biomedical publications domain, especially selected due to its
importance and inherent challenges. A simple machine learning
approach is used that eliminates prior language knowledge such as
part-of-speech or noun phrase tagging thereby allowing for its
applicability across languages. No domain-specific knowledge is
included. The accuracy measures achieved are comparable to those
obtained using more complex approaches, which constitutes a
motivation to investigate ways to improve the scalability of multiclass
SVM in order to make the solution more practical and useable.
Improving training time of multi-class SVM would make support
vector machines a more viable and practical machine learning
solution for real-world problems with large datasets. An initial
prototype results in great improvement of the training time at the
expense of memory requirements.

This paper proposes a modeling methodology for the
development of data analysis solution. The Author introduce the
approach to address data warehousing issues at the at enterprise level.
The methodology covers the process of the requirements eliciting and
analysis stage as well as initial design of data warehouse. The paper
reviews extended business process model, which satisfy the needs of
data warehouse development. The Author considers that the use of
business process models is necessary, as it reflects both enterprise
information systems and business functions, which are important for
data analysis. The Described approach divides development into
three steps with different detailed elaboration of models. The
Described approach gives possibility to gather requirements and
display them to business users in easy manner.

In this paper, stabilization of an Active Magnetic Bearing (AMB) system with varying rotor speed using Sliding Mode Control (SMC) technique is considered. The gyroscopic effect inherited in the system is proportional to rotor speed in which this nonlinearity effect causes high system instability as the rotor speed increases. Also, transformation of the AMB dynamic model into a new class of uncertain system shows that this gyroscopic effect lies in the mismatched part of the system matrix. Moreover, the current gain parameter is allowed to be varied in a known bound as an uncertainty in the input matrix. SMC design method is proposed in which the sufficient condition that guarantees the global exponential stability of the reduced-order system is represented in Linear Matrix Inequality (LMI). Then, a new chattering-free control law is established such that the system states are driven to reach the switching surface and stay on it thereafter. The performance of the controller applied to the AMB model is demonstrated through simulation works under various system conditions.

In this paper, application of Sliding Mode Control (SMC) technique for an Active Magnetic Bearing (AMB) system with varying rotor speed is considered. The gyroscopic effect and mass imbalance inherited in the system is proportional to rotor speed in which this nonlinearity effect causes high system instability as the rotor speed increases. Transformation of the AMB dynamic model into regular system shows that these gyroscopic effect and imbalance lie in the mismatched part of the system. A H2-based sliding surface is designed which bound the mismatched parts. The solution of the surface parameter is obtained using Linear Matrix Inequality (LMI). The performance of the controller applied to the AMB model is demonstrated through simulation works under various system conditions.

Passive systems were born with the purpose of the
greatest exploitation of solar energy in cold climates and high
altitudes. They spread themselves until the 80-s all over the world
without any attention to the specific climate and the summer
behavior; this caused the deactivation of the systems due to a series
of problems connected to the summer overheating, the complex
management and the rising of the dust.
Until today the European regulation limits only the winter
consumptions without any attention to the summer behavior but, the
recent European EN 15251 underlines the relevance of the indoor
comfort, and the necessity of the analytic studies validation by
monitoring case studies.
In the porpose paper we demonstrate that the solar wall is an
efficient system both from thermal comfort and energy saving point
of view and it is the most suitable for our temperate climates because
it can be used as a passive cooling sistem too. In particular the paper
present an experimental and numerical analisys carried out on a case
study with nine different solar passive systems in Ancona, Italy.
We carried out a detailed study of the lodging provided by the
solar wall by the monitoring and the evaluation of the indoor
conditions.
Analyzing the monitored data, on the base of recognized models
of comfort (ISO, ASHRAE, Givoni-s BBCC), is emerged that the
solar wall has an optimal behavior in the middle seasons. In winter
phase this passive system gives more advantages in terms of energy
consumptions than the other systems, because it gives greater heat
gain and therefore smaller consumptions. In summer, when outside
air temperature return in the mean seasonal value, the indoor comfort
is optimal thanks to an efficient transversal ventilation activated from
the same wall.

Both the minimum energy consumption and
smoothness, which is quantified as a function of jerk, are generally
needed in many dynamic systems such as the automobile and the
pick-and-place robot manipulator that handles fragile equipments.
Nevertheless, many researchers come up with either solely
concerning on the minimum energy consumption or minimum jerk
trajectory. This research paper proposes a simple yet very interesting
relationship between the minimum direct and indirect jerks
approaches in designing the time-dependent system yielding an
alternative optimal solution. Extremal solutions for the cost functions
of direct and indirect jerks are found using the dynamic optimization
methods together with the numerical approximation. This is to allow
us to simulate and compare visually and statistically the time history
of control inputs employed by minimum direct and indirect jerk
designs. By considering minimum indirect jerk problem, the
numerical solution becomes much easier and yields to the similar
results as minimum direct jerk problem.

The performance of a sucrose-based H2 production in
a completely stirred tank reactor (CSTR) was modeled by neural
network back-propagation (BP) algorithm. The H2 production was
monitored over a period of 450 days at 35±1 ºC. The proposed model
predicts H2 production rates based on hydraulic retention time
(HRT), recycle ratio, sucrose concentration and degradation, biomass
concentrations, pH, alkalinity, oxidation-reduction potential (ORP),
acids and alcohols concentrations. Artificial neural networks (ANNs)
have an ability to capture non-linear information very efficiently. In
this study, a predictive controller was proposed for management and
operation of large scale H2-fermenting systems. The relevant control
strategies can be activated by this method. BP based ANNs modeling
results was very successful and an excellent match was obtained
between the measured and the predicted rates. The efficient H2
production and system control can be provided by predictive control
method combined with the robust BP based ANN modeling tool.

Verification of real-time software systems can be
expensive in terms of time and resources. Testing is the main method
of proving correctness but has been shown to be a long and time
consuming process. Everyday engineers are usually unwilling to
adopt formal approaches to correctness because of the overhead
associated with developing their knowledge of such techniques.
Performance modelling techniques allow systems to be evaluated
with respect to timing constraints. This paper describes PARTES, a
framework which guides the extraction of performance models from
programs written in an annotated subset of C.

The objective of this paper is the introduction to a
unified optimization framework for research and education. The
OPTILIB framework implements different general purpose algorithms
for combinatorial optimization and minimum search on standard continuous
test functions. The preferences of this library are the straightforward
integration of new optimization algorithms and problems
as well as the visualization of the optimization process of different
methods exploring the search space exclusively or for the real time
visualization of different methods in parallel. Further the usage of
several implemented methods is presented on the basis of two use
cases, where the focus is especially on the algorithm visualization.
First it is demonstrated how different methods can be compared
conveniently using OPTILIB on the example of different iterative
improvement schemes for the TRAVELING SALESMAN PROBLEM.
A second study emphasizes how the framework can be used to find
global minima in the continuous domain.

A word recognition architecture based on a network
of neural associative memories and hidden Markov models has been
developed. The input stream, composed of subword-units like wordinternal
triphones consisting of diphones and triphones, is provided
to the network of neural associative memories by hidden Markov
models. The word recognition network derives words from this input
stream. The architecture has the ability to handle ambiguities on
subword-unit level and is also able to add new words to the
vocabulary during performance. The architecture is implemented to
perform the word recognition task in a language processing system
for understanding simple command sentences like “bot show apple".